SImageSet-class {Cardinal} | R Documentation |
An iSet
derived class for pixel-sparse imaging data. Data is stored to be memory efficient when there are missing pixels or when the the stored images are non-rectangular regions. The data structures remain efficient for non-sparse pixel data as long as the full datacube does not need to be reconstructed often, and single images or feature vectors are of primary interest. This class can be combined with Hashmat
to be sparse in both feature space and pixel space. This is useful for datasets with sparse signals, such as processed spectra.
MSImageSet
is a derived class of SImageSet
for storing mass spectrometry imaging experiments.
## Instance creation SImageSet( data = Hashmat(nrow=0, ncol=0), coord = expand.grid( x = seq_len(prod(dim(data)[-1])), y = seq_len(ifelse(prod(dim(data)[-1]) > 0, 1, 0))), imageData = SImageData( data=data, coord=coord), pixelData = IAnnotatedDataFrame( data=coord, varMetadata=data.frame(labelType=rep("dim", ncol(coord)))), featureData = AnnotatedDataFrame( data=data.frame(row.names=seq_len(nrow(data)))), protocolData = AnnotatedDataFrame( data=data.frame(row.names=sampleNames(pixelData))), experimentData = new("MIAPE-Imaging"), ...) ## Additional methods documented below
data |
A matrix-like object with number of rows equal to the number of features and number of columns equal to the number of non-missing pixels. Each column should be a feature vector. Alternatively, a multidimensional array that represents the datacube with the first dimension as the features can also be supplied. Additional dimensions could be the spatial dimensions of the image, for example. |
coord |
A |
imageData |
An object of class |
pixelData |
An object of class |
featureData |
An object of class |
protocolData |
An object of class |
experimentData |
An object derived from class |
... |
Additional arguments passed to the initializer. |
imageData
:An instance of SImageData
, which stores one or more matrices of equal number of dimensions as elements in an 'immutableEnvironment'. This slot preserves copy-on-write behavior when it is modified specifically, but is pass-by-reference otherwise, for memory efficiency.
pixelData
:Contains pixel information in an IAnnotatedDataFrame
. This includes both pixel coordinates and phenotypic and sample data. Its rows correspond to the columns in imageData
.
featureData
:Contains variables describing features in an IAnnotatedDataFrame
. Its rows correspond to the rows in imageData
.
experimentData
:Contains details of experimental methods. Should be an object of a derived class of MIAxE
.
protocolData
:Contains variables in an IAnnotatedDataFrame
describing the generation of the samples in pixelData
.
.__classVersion__
:A Versions
object describing the version of the class used to created the instance. Intended for developer use.
iSet
, directly.
VersionedBiobase
, by class "iSet", distance 1.
Versioned
, by class "VersionedBiobase", distance 2.
SImageSet
instances are usually created through SImageSet()
.
Class-specific methods:
iData(object), iData(object) <- value
:Access and set the sparse image data in imageData
. This is a matrix-like object with rows corresponding to features and columns corresponding to pixels, so that each column of the returned object is a feature vector.
regeneratePositions
:Regenerates the positionArray
in imageData
used to reconstruct the datacube based on the coordinates in pixelData
. Normally, this should not be called by the user. However, if the coordinates are modified manually, it can be used to re-sync the data structures.
Standard generic methods:
combine(x, y, ...)
:Combine two or more SImageSet
objects. Unique 'sample's in pixelData
are treated as a dimension.
SImageSet[i, j, ..., drop]
:Subset an SImageSet
based on the rows (featureData components) and the columns (pixelData components). The result is a new SImageSet
.
See iSet
for additional methods.
Kylie A. Bemis
## Create an SImageSet object data <- matrix(1:27, nrow=3) coord <- expand.grid(x=1:3, y=1:3) sset <- SImageSet(data=data, coord=coord) ## Access a single image corresponding to the first feature imageData(sset)[1,,] ## Reconstruct the datacube imageData(sset)[] ## Access the P x N matrix of column-wise feature vectors iData(sset) ## Subset the SImageSet to the first 2 features and first 6 pixels sset2 <- sset[1:2, 1:6] imageData(sset2)[] sset2